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A10G
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import os
import time
from omegaconf import OmegaConf
import torch
from scripts.evaluation.funcs import load_model_checkpoint, load_image_batch, save_videos, batch_ddim_sampling
from utils.utils import instantiate_from_config
from huggingface_hub import hf_hub_download
class Image2Video():
def __init__(self,result_dir='./tmp/',gpu_num=1) -> None:
self.download_model()
self.result_dir = result_dir
if not os.path.exists(self.result_dir):
os.mkdir(self.result_dir)
ckpt_path='checkpoints/i2v_512_v1/model.ckpt'
config_file='configs/inference_i2v_512_v1.0.yaml'
config = OmegaConf.load(config_file)
model_config = config.pop("model", OmegaConf.create())
model_config['params']['unet_config']['params']['use_checkpoint']=False
model_list = []
for gpu_id in range(gpu_num):
model = instantiate_from_config(model_config)
# model = model.cuda(gpu_id)
assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
model = load_model_checkpoint(model, ckpt_path)
model.eval()
model_list.append(model)
self.model_list = model_list
self.save_fps = 8
def get_image(self, image, prompt, steps=50, cfg_scale=12.0, eta=1.0, fps=16):
torch.cuda.empty_cache()
print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))
start = time.time()
gpu_id=0
if steps > 60:
steps = 60
model = self.model_list[gpu_id]
model = model.cuda()
batch_size=1
channels = model.model.diffusion_model.in_channels
frames = model.temporal_length
h, w = 320 // 8, 512 // 8
noise_shape = [batch_size, channels, frames, h, w]
#prompts = batch_size * [""]
text_emb = model.get_learned_conditioning([prompt])
# cond_images = load_image_batch([image_path])
img_tensor = torch.from_numpy(image).permute(2, 0, 1).float()
img_tensor = (img_tensor / 255. - 0.5) * 2
img_tensor = img_tensor.unsqueeze(0)
cond_images = img_tensor.to(model.device)
img_emb = model.get_image_embeds(cond_images)
imtext_cond = torch.cat([text_emb, img_emb], dim=1)
cond = {"c_crossattn": [imtext_cond], "fps": fps}
## inference
batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
## b,samples,c,t,h,w
prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt
prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str
prompt_str=prompt_str[:30]
save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps)
print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds")
model = model.cpu()
return os.path.join(self.result_dir, f"{prompt_str}.mp4")
def download_model(self):
REPO_ID = 'VideoCrafter/Image2Video-512'
filename_list = ['model.ckpt']
if not os.path.exists('./checkpoints/i2v_512_v1/'):
os.makedirs('./checkpoints/i2v_512_v1/')
for filename in filename_list:
local_file = os.path.join('./checkpoints/i2v_512_v1/', filename)
if not os.path.exists(local_file):
hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/i2v_512_v1/', local_dir_use_symlinks=False)
if __name__ == '__main__':
i2v = Image2Video()
video_path = i2v.get_image('prompts/i2v_prompts/horse.png','horses are walking on the grassland')
print('done', video_path) |